Reliability-oriented sensitivity analysis methods have been developed for understanding the influence of model inputs relatively to events characterizing the failure of a system (e.g., a threshold exceedance of the model output). In this field, the target sensitivity analysis focuses primarily on capturing the influence of the inputs on the occurrence of such a critical event. This paper proposes new target sensitivity indices, based on the Shapley values and called "target Shapley effects", allowing for interpretable influence measures of each input in the case of dependence between the inputs. Two algorithms (a Monte Carlo sampling one, and a given-data algorithm) are proposed for the estimation of these target Shapley effects based on the $\ell^2$ norm. Additionally, the behavior of these target Shapley effects are theoretically and empirically studied through various toy-cases. Finally, applications on two realistic use-cases (a river flood model and a COVID-19 epidemiological model) are discussed.

翻译：为了解模型投入相对于系统失灵特征事件的影响(例如,模型输出的临界值超过临界值),制定了注重可靠性的敏感度分析方法,以了解模型投入相对于系统失灵特征事件的影响(例如,模型输出的临界值超过临界值),在这方面,目标敏感度分析主要侧重于捕捉投入对发生这种重大事件的影响,本文件根据“变色值”提出了新的目标敏感度指数,称为“目标光谱效应”,允许对投入依赖性情况下的每项投入进行可解释的影响计量,并提出了两种算法(蒙特卡洛取样法和特定数据算法),用于根据$\ell ⁇ 2$的规范估算这些目标“变色效应”的影响,此外,这些目标的“变色效应”行为在理论上和经验上通过各种小案例进行了研究,最后讨论了两种实际使用案例(河流洪水模型和COVID-19流行病学模型)的应用。